Previous works mainly focused on estimating direct relationship strength in social networks. If two users are not directly connected in a social network, there is no direct relationship. In order to estimate the relat...Previous works mainly focused on estimating direct relationship strength in social networks. If two users are not directly connected in a social network, there is no direct relationship. In order to estimate the relationship strength between two indirectly connected users as well as directly connected users, this paper proposes an estimation method for relationship strength in weighted social network graphs, which is based on the trust propagation strategy and the estimation of direct relationship strength. Our method considers the length of a relationship path, the number of relationship paths and the edge weights (direct relationship strength) along with a relationship path to estimate the strength of indirect relationship. Then it synthesizes the direct and indirect relationship strength to represent the strength of relationship between two users in social net- works. Thus our method can fully estimate the relationship strength between any two users in a social network no matter whether they are directly connected or not.展开更多
An artificial neural network(ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar(SHPB) exper...An artificial neural network(ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnsone Cook(Je C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures(500e650 C), strains(0.05e0.2) and strain rates(1000e5500/s) are employed to formulate Je C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient(R) and average absolute relative error(AARE). R and AARE for the Je C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.展开更多
To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned paramete...To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned parameters consisted of graphite content, maximum graphite length, primary dendrite percentage and microhardness of the matrix. Under the superposed influence of various parameters, the relationships between thermal conductivity and structural characteristics become irregular, as well as the effects of graphite length on the strength. An adaptive neuro-fuzzy inference system was built to link the parameters and properties. A sensitivity test was then performed to rank the relative impact of parameters. It was found that the dominant parameter for tensile strength is graphite content, while the most relative parameter for thermal conductivity is maximum graphite length. The most effective method to simultaneously improve the tensile and thermal conductivity of gray cast iron is to reduce the carbon equivalent and increase the length of graphite flakes.展开更多
Traffic is an indispensable prerequisite for a tourism system. The ＂four vertical and four horizontal＂ HSR network represents an important milestone of the ＂traffic revolution＂ in China. It will affect the spatial...Traffic is an indispensable prerequisite for a tourism system. The ＂four vertical and four horizontal＂ HSR network represents an important milestone of the ＂traffic revolution＂ in China. It will affect the spatial pattern of tourism accessibility in Chinese cities, thus substan- tially increasing their power to attract tourists and their radiation force. This paper examines the evolution and spatial characteristics of the power to attract tourism of cities linked by China＇s HSR network by measuring the influence of accessibility of 338 HSR-linked cities using GIS analysis. The results show the following. （1） The accessibility of Chinese cities is optimized by the HSR network, whose spatial pattern of accessibility exhibits an obvious traf- fic direction and causes a high-speed rail-corridor effect. （2） The spatial pattern of tourism field strength in Chinese cities exhibits the dual characteristics of multi-center annular diver- gence and dendritic diffusion. Dendritic diffusion is particularly more obvious along the HSR line. The change rate of urban tourism field strength forms a high-value corridor along the HSR line and exhibits a spatial pattern of decreasing area from the center to the outer limit along the HSR line. （3） The influence of the higher and highest tourism field strength areas along the HSR line is most significant, and the number of cities that distribute into these two types of tourism field strengths significantly increases： their area expands by more than 100% HSR enhances the tourism field strength value of regional central cities, and the radiation range of tourism attraction extends along the HSR line.展开更多
In this paper, a simple model for the strength dynamics of weighted evolving networks is proposed to characterize the weighted networks. By considering the congestion effects, this approach can yield power law strengt...In this paper, a simple model for the strength dynamics of weighted evolving networks is proposed to characterize the weighted networks. By considering the congestion effects, this approach can yield power law strength distribution appeared on the many real weighted networks, such as traffic networks, internet networks. Besides, the relationship between strength and degree is given. Numerical simulations indicate that the strength distribution is strongly related to the strength dynamics decline. The model also provides us with a better description of the real weighted networks.展开更多
In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser...In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.展开更多
The Sonreb and Core (SRC) combined method is proposed to assess the concrete compression strength of mass concrete structures.Artificial neural network is employed together with the SRC combined method to obtain the o...The Sonreb and Core (SRC) combined method is proposed to assess the concrete compression strength of mass concrete structures.Artificial neural network is employed together with the SRC combined method to obtain the optimal core number.The artificial neural network is trained based on data from different testing methods.The procedure of using artificial neural network to assess the concrete strength is described.It proves that the SRC combined method is superior in many aspects and artificial the presented neural network has a high efficiency and reliability.The combined method using artificial intelligence is promising in the strength assessment of mass concrete structures such as the dam,the anchor of the suspension bridge,etc.展开更多
Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into t...Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.展开更多
Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of s...Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks（FN） using data available in the literature. The performance of FN was compared with support vector machine（SVM） and artificial neural network（ANN） based on statistical parameters like correlation coefficient（R）, Nash–Sutcliff coefficient of efficiency（E）, absolute average error（AAE）, maximum average error（MAE） and root mean square error（RMSE）. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.展开更多
Stiffened Panels are important strength members in ship and offshore structures. A new method based on counterpropagation neural networks (CPN) is proposed in this paper to predict the ultimate compressive strength of...Stiffened Panels are important strength members in ship and offshore structures. A new method based on counterpropagation neural networks (CPN) is proposed in this paper to predict the ultimate compressive strength of stiffened panels. Compared with two-parametric polynomial, this method can take more parameters into account and make more use of experimental data. Numerical study is carried out to verify the validation of this method. The new method may find wide application in practical design.展开更多
Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities ...Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world （SW） connections. Network elements are described by two-dimensional map neurons （2DMNs） with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied.展开更多
In this paper, we propose a novel approach for simultaneously identifying unknown parameters and synchronizing time-delayed complex community networks with nonidentical nodes. Based on the LaSalle＇s invariance princi...In this paper, we propose a novel approach for simultaneously identifying unknown parameters and synchronizing time-delayed complex community networks with nonidentical nodes. Based on the LaSalle＇s invariance principle, a cri- teflon is established by constructing an effective control identification scheme and adjusting automatically the adaptive coupling strength. The proposed control law is applied to a complex community network which is periodically synchro- nized with different chaotic states. Numerical simulations are conducted to demonstrate the feasibility of the proposed method.展开更多
The interpenetrating polymer network(IPN) silicone hydrogels with improved oxygen permeability and mechanical strength were prepared by UV-initiated polymerization of monomers including methacryloxypropyl tris(trimeth...The interpenetrating polymer network(IPN) silicone hydrogels with improved oxygen permeability and mechanical strength were prepared by UV-initiated polymerization of monomers including methacryloxypropyl tris(trimethylsiloxy)silane(TRIS),2-hydroxyethylmethacrylate(HEMA) and N-vinyl pyrrolidone(NVP) in the presence of free radical photoinitiator and cationic photoinitiator.The polymerization mechanism was investigated by the formation of gel network.The structure of IPN hydrogels was characterized by Fourier transform infrared spectroscopy(FTIR), differential scanning calorimetry(DSC) and transmission electron microscopy(TEM).The results showed that the IPN hydrogels exhibited a heterogeneous morphology.The mechanical properties,surface wettability and oxygen permeability were examined by using a tensile tester,a contact angle goniometer and an oxygen transmission tester,respectively.The equilibrium water content of the hydrogels was measured by the gravimetric method.The results revealed that the IPN hydrogels possessed hydrophilic surface and high water content.They exhibited improved oxygen permeability and mechanical strength because of the incorporation of TRIS.展开更多
Developing a low-cost and well-recyclable adsorbent with high adsorption capacity is greatly desirable in dye wastewater treatment. Here, we demonstrate a kind of novel tough and reusable hydrogel beads with quite hig...Developing a low-cost and well-recyclable adsorbent with high adsorption capacity is greatly desirable in dye wastewater treatment. Here, we demonstrate a kind of novel tough and reusable hydrogel beads with quite high capacity of dye adsorption via incorporating mussel-bioinspired poly（L-DOPA） （PDOPA） into alginate/poly（acrylamide） double network （DN） hydrogels. The synthesized PDOPA nanoaggregates were introduced into the DN hydrogels by simple one-pot mixing with the monomers prior to polymerization. The fabricated hydrogel beads exhibited high mechanical strength and good elastic recovery due to the interpenetrating Ca2＋-alginate and poly（acrylamide） networks. It was shown that the beads exhibited relatively high dye adsorption capacity compared to other adsorbents reported in literature, and the introduction of PDOPA with an appropriate amount raised the adsorption capacity. It is believed that the addition of PDOPA and the matrix of double network architecture contributed synergistically to the high adsorption capacity of hydrogel beads. Moreover, the desorption of dyes could be easily realized via rinsing in acidic water and ethanol solution. The hydrogel beads remained the high adsorption capacity even after 5 times of adsorption and desorption cycles. This tough and stable hydrogel with high adsorption capacity may have potential in treatment of dye wastewater released by textile dyeing industry.展开更多
Overhanging rock slopes(steeper than 90°) are typically avoided in rock engineering design, particularly where the scale of the slope exceeds the scale of fracturing present in the rock mass. This paper highlight...Overhanging rock slopes(steeper than 90°) are typically avoided in rock engineering design, particularly where the scale of the slope exceeds the scale of fracturing present in the rock mass. This paper highlights an integrated approach of designing overhanging rock slopes where the relative dimensions of the slope exceed the scale of fracturing and the rock mass failure needs to be considered rather than kinematic release of individual blocks. The key to the method is a simplified limit equilibrium(LE) tool that was used for the support design and analysis of a multi-faceted overhanging rock slope. The overhanging slopes required complex geometries with constantly changing orientations. The overhanging rock varied in height from 30 m to 66 m. Geomechanical modelling combined with discrete fracture network(DFN)representation of the rock mass was used to validate the rock mass strength assumptions and the failure mechanism assumed in the LE model. The advantage of the simplified LE method is that buttress and support design iterations(along with sensitivity analysis of design parameters) can be completed for various cross-sections along the proposed overhanging rock sections in an efficient manner, compared to the more time-intensive, sophisticated methods that were used for the initial validation. The method described presents the development of this design tool and assumptions made for a specific overhanging rock slope design. Other locations will have different geological conditions that can control the potential behaviour of rock slopes, however, the approach presented can be applied as a general guiding design principle for overhanging rock cut slope.展开更多
This paper studies the stochastic synchronization problem for time-varying complex dynamical networks. This model is totally different from some existing network models. Based on the Lyapunov stability theory, inequal...This paper studies the stochastic synchronization problem for time-varying complex dynamical networks. This model is totally different from some existing network models. Based on the Lyapunov stability theory, inequality techniques, and the properties of the Weiner process, some controllers and adaptive laws are designed to ensure achieving stochastic synchronization of a complex dynamical network model. A sufficient synchronization condition is given to ensure that the proposed network model is mean-square stable. Theoretical analysis and numerical simulation fully verify the main results.展开更多
In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing ZrO2 nanoparticles have bee...In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing ZrO2 nanoparticles have been developed at different ages of curing. For building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models were arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models, the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles. It has been found that neural network （NN） and gene expression programming （GEP） models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.展开更多
文摘Previous works mainly focused on estimating direct relationship strength in social networks. If two users are not directly connected in a social network, there is no direct relationship. In order to estimate the relationship strength between two indirectly connected users as well as directly connected users, this paper proposes an estimation method for relationship strength in weighted social network graphs, which is based on the trust propagation strategy and the estimation of direct relationship strength. Our method considers the length of a relationship path, the number of relationship paths and the edge weights (direct relationship strength) along with a relationship path to estimate the strength of indirect relationship. Then it synthesizes the direct and indirect relationship strength to represent the strength of relationship between two users in social net- works. Thus our method can fully estimate the relationship strength between any two users in a social network no matter whether they are directly connected or not.
文摘An artificial neural network(ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnsone Cook(Je C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures(500e650 C), strains(0.05e0.2) and strain rates(1000e5500/s) are employed to formulate Je C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient(R) and average absolute relative error(AARE). R and AARE for the Je C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.
文摘To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned parameters consisted of graphite content, maximum graphite length, primary dendrite percentage and microhardness of the matrix. Under the superposed influence of various parameters, the relationships between thermal conductivity and structural characteristics become irregular, as well as the effects of graphite length on the strength. An adaptive neuro-fuzzy inference system was built to link the parameters and properties. A sensitivity test was then performed to rank the relative impact of parameters. It was found that the dominant parameter for tensile strength is graphite content, while the most relative parameter for thermal conductivity is maximum graphite length. The most effective method to simultaneously improve the tensile and thermal conductivity of gray cast iron is to reduce the carbon equivalent and increase the length of graphite flakes.
基金Foundation： National Natural Science Foundation of China, No. 41271134
文摘Traffic is an indispensable prerequisite for a tourism system. The ＂four vertical and four horizontal＂ HSR network represents an important milestone of the ＂traffic revolution＂ in China. It will affect the spatial pattern of tourism accessibility in Chinese cities, thus substan- tially increasing their power to attract tourists and their radiation force. This paper examines the evolution and spatial characteristics of the power to attract tourism of cities linked by China＇s HSR network by measuring the influence of accessibility of 338 HSR-linked cities using GIS analysis. The results show the following. （1） The accessibility of Chinese cities is optimized by the HSR network, whose spatial pattern of accessibility exhibits an obvious traf- fic direction and causes a high-speed rail-corridor effect. （2） The spatial pattern of tourism field strength in Chinese cities exhibits the dual characteristics of multi-center annular diver- gence and dendritic diffusion. Dendritic diffusion is particularly more obvious along the HSR line. The change rate of urban tourism field strength forms a high-value corridor along the HSR line and exhibits a spatial pattern of decreasing area from the center to the outer limit along the HSR line. （3） The influence of the higher and highest tourism field strength areas along the HSR line is most significant, and the number of cities that distribute into these two types of tourism field strengths significantly increases： their area expands by more than 100% HSR enhances the tourism field strength value of regional central cities, and the radiation range of tourism attraction extends along the HSR line.
基金Project supported by the National 0utstanding Young Investigator of China （Grant No 70225005）, and the National Natural Science Foundation of China （Grant Nos 70501005 and 70501004）, and the Natural Science Foundation of Beijing （Grant No 9042006）, the Innovation Foundation of Science and Technology for Excellent Doctorial Candidate of Beijing Jiaotong University, China （Grant No 48006） and the National Basic Research Program of China （Grant No 2006CB705500）.
文摘In this paper, a simple model for the strength dynamics of weighted evolving networks is proposed to characterize the weighted networks. By considering the congestion effects, this approach can yield power law strength distribution appeared on the many real weighted networks, such as traffic networks, internet networks. Besides, the relationship between strength and degree is given. Numerical simulations indicate that the strength distribution is strongly related to the strength dynamics decline. The model also provides us with a better description of the real weighted networks.
基金National Natural Science Foundation of China(No.51475315)Innovative Project on the Integration of Industry,Education and Research of Jiangsu Province,China(No.BY2014059-10)
文摘In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.
基金Sponsored by the Priority Academic Program Development Foundation of Jiangsu Higher Education Institute(Grant No. CE01-3)the NSFC for Outstanding Youth Fund (Grant No. 50725828),the NSFC for Young Scholars (Grant No. 50908046)+1 种基金the Ph. D. Programs Foundation of Ministry of Education of China (Grant No. 200802861012)the Basic Scientific & Research Fund of Southeast University (Grant No. Seucx201106)
文摘The Sonreb and Core (SRC) combined method is proposed to assess the concrete compression strength of mass concrete structures.Artificial neural network is employed together with the SRC combined method to obtain the optimal core number.The artificial neural network is trained based on data from different testing methods.The procedure of using artificial neural network to assess the concrete strength is described.It proves that the SRC combined method is superior in many aspects and artificial the presented neural network has a high efficiency and reliability.The combined method using artificial intelligence is promising in the strength assessment of mass concrete structures such as the dam,the anchor of the suspension bridge,etc.
基金supported by the National Natural Science Foundation of China (No.51273189)the National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2016ZX05016),the National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2016ZX05046)
文摘Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.
文摘Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks（FN） using data available in the literature. The performance of FN was compared with support vector machine（SVM） and artificial neural network（ANN） based on statistical parameters like correlation coefficient（R）, Nash–Sutcliff coefficient of efficiency（E）, absolute average error（AAE）, maximum average error（MAE） and root mean square error（RMSE）. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.
文摘Stiffened Panels are important strength members in ship and offshore structures. A new method based on counterpropagation neural networks (CPN) is proposed in this paper to predict the ultimate compressive strength of stiffened panels. Compared with two-parametric polynomial, this method can take more parameters into account and make more use of experimental data. Numerical study is carried out to verify the validation of this method. The new method may find wide application in practical design.
基金Project supported by the Key Program of National Natural Science Foundation of China (Grant No. 50937001),the National Natural Science Foundation of China (Grant Nos. 50877028, 10947011 and 10862001)the High Technology Research and Development Program of China (Grant No. 2007AA05Z229)+1 种基金the Science Foundation of Guangdong Province, China (Grant No. 8251064101000014)the Construction of Key Laboratories in Universities of Guangxi Province, China (Grant No. 200912)
文摘Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world （SW） connections. Network elements are described by two-dimensional map neurons （2DMNs） with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied.
基金Project supported by the Key Program of the National Natural Science of China(Grant No.11232009)the Shanghai Leading Academic Discipline Project,China(Grant No.S30106)
文摘In this paper, we propose a novel approach for simultaneously identifying unknown parameters and synchronizing time-delayed complex community networks with nonidentical nodes. Based on the LaSalle＇s invariance principle, a cri- teflon is established by constructing an effective control identification scheme and adjusting automatically the adaptive coupling strength. The proposed control law is applied to a complex community network which is periodically synchro- nized with different chaotic states. Numerical simulations are conducted to demonstrate the feasibility of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.50573011 and 50673019)
文摘The interpenetrating polymer network(IPN) silicone hydrogels with improved oxygen permeability and mechanical strength were prepared by UV-initiated polymerization of monomers including methacryloxypropyl tris(trimethylsiloxy)silane(TRIS),2-hydroxyethylmethacrylate(HEMA) and N-vinyl pyrrolidone(NVP) in the presence of free radical photoinitiator and cationic photoinitiator.The polymerization mechanism was investigated by the formation of gel network.The structure of IPN hydrogels was characterized by Fourier transform infrared spectroscopy(FTIR), differential scanning calorimetry(DSC) and transmission electron microscopy(TEM).The results showed that the IPN hydrogels exhibited a heterogeneous morphology.The mechanical properties,surface wettability and oxygen permeability were examined by using a tensile tester,a contact angle goniometer and an oxygen transmission tester,respectively.The equilibrium water content of the hydrogels was measured by the gravimetric method.The results revealed that the IPN hydrogels possessed hydrophilic surface and high water content.They exhibited improved oxygen permeability and mechanical strength because of the incorporation of TRIS.
基金supported by the National Natural Science Foundation of China(Nos.51573159 and 51273176)the Fundamental Research Funds for the Central Universities(No.2016QNA4032)
文摘Developing a low-cost and well-recyclable adsorbent with high adsorption capacity is greatly desirable in dye wastewater treatment. Here, we demonstrate a kind of novel tough and reusable hydrogel beads with quite high capacity of dye adsorption via incorporating mussel-bioinspired poly（L-DOPA） （PDOPA） into alginate/poly（acrylamide） double network （DN） hydrogels. The synthesized PDOPA nanoaggregates were introduced into the DN hydrogels by simple one-pot mixing with the monomers prior to polymerization. The fabricated hydrogel beads exhibited high mechanical strength and good elastic recovery due to the interpenetrating Ca2＋-alginate and poly（acrylamide） networks. It was shown that the beads exhibited relatively high dye adsorption capacity compared to other adsorbents reported in literature, and the introduction of PDOPA with an appropriate amount raised the adsorption capacity. It is believed that the addition of PDOPA and the matrix of double network architecture contributed synergistically to the high adsorption capacity of hydrogel beads. Moreover, the desorption of dyes could be easily realized via rinsing in acidic water and ethanol solution. The hydrogel beads remained the high adsorption capacity even after 5 times of adsorption and desorption cycles. This tough and stable hydrogel with high adsorption capacity may have potential in treatment of dye wastewater released by textile dyeing industry.
文摘Overhanging rock slopes(steeper than 90°) are typically avoided in rock engineering design, particularly where the scale of the slope exceeds the scale of fracturing present in the rock mass. This paper highlights an integrated approach of designing overhanging rock slopes where the relative dimensions of the slope exceed the scale of fracturing and the rock mass failure needs to be considered rather than kinematic release of individual blocks. The key to the method is a simplified limit equilibrium(LE) tool that was used for the support design and analysis of a multi-faceted overhanging rock slope. The overhanging slopes required complex geometries with constantly changing orientations. The overhanging rock varied in height from 30 m to 66 m. Geomechanical modelling combined with discrete fracture network(DFN)representation of the rock mass was used to validate the rock mass strength assumptions and the failure mechanism assumed in the LE model. The advantage of the simplified LE method is that buttress and support design iterations(along with sensitivity analysis of design parameters) can be completed for various cross-sections along the proposed overhanging rock sections in an efficient manner, compared to the more time-intensive, sophisticated methods that were used for the initial validation. The method described presents the development of this design tool and assumptions made for a specific overhanging rock slope design. Other locations will have different geological conditions that can control the potential behaviour of rock slopes, however, the approach presented can be applied as a general guiding design principle for overhanging rock cut slope.
基金supported by the National Natural Science Foundation of China (Grant No.60974139)the Fundamental Research Funds for the Central Universities (Grant No.72103676)
文摘This paper studies the stochastic synchronization problem for time-varying complex dynamical networks. This model is totally different from some existing network models. Based on the Lyapunov stability theory, inequality techniques, and the properties of the Weiner process, some controllers and adaptive laws are designed to ensure achieving stochastic synchronization of a complex dynamical network model. A sufficient synchronization condition is given to ensure that the proposed network model is mean-square stable. Theoretical analysis and numerical simulation fully verify the main results.
文摘In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing ZrO2 nanoparticles have been developed at different ages of curing. For building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models were arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models, the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles. It has been found that neural network （NN） and gene expression programming （GEP） models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.
